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<p>Fish distribution in the lagoon complex of Lake Nokoué and Porto-Novo Lagoon is poorly documented. The lagoon complex is of particular interest because of its contribution to the local supply of animal protein. The fish communities in the complex were studied monthly between January and December 2015 at six sampling sites using two sets of gillnets with varying mesh sizes. The study applied a non-linear self-organizing map (SOM) method to classify the sampling sites based on the composition of fish assemblages, and an index of indicator value (IndVal) was calculated for each species identified. In total, 58 species from 49 genera and 34 families were cataloged with 45 species observed in Lake Nokoué and 48 in the Porto-Novo Lagoon. Notably, 35 species were common to both water bodies. The analysis revealed a dominance of occasional continental (20.7%) and strict estuarine species (20.7%). The SOM analysis suggested a tripartite structure of the fish communities, largely influenced by the geographical positioning of the sampling sites. The IndVal method identified 28 indicator species in the typical assemblages delineated by the SOM approach. The study highlights the importance of these indicator species for the conservation efforts aimed at sustaining the ichthyofauna of the lagoon complex.</p>
</abstract>

Fish distribution in the lagoon complex of Lake Nokoué and Porto-Novo Lagoon is poorly documented. The lagoon complex is of particular interest because of its contribution to the local supply of animal protein. The fish communities in the complex were studied monthly between January and December 2015 at six sampling sites using two sets of gillnets with varying mesh sizes. The study applied a non-linear self-organizing map (SOM) method to classify the sampling sites based on the composition of fish assemblages, and an index of indicator value (IndVal) was calculated for each species identified. In total, 58 species from 49 genera and 34 families were cataloged with 45 species observed in Lake Nokoué and 48 in the Porto-Novo Lagoon. Notably, 35 species were common to both water bodies. The analysis revealed a dominance of occasional continental (20.7%) and strict estuarine species (20.7%). The SOM analysis suggested a tripartite structure of the fish communities, largely influenced by the geographical positioning of the sampling sites. The IndVal method identified 28 indicator species in the typical assemblages delineated by the SOM approach. The study highlights the importance of these indicator species for the conservation efforts aimed at sustaining the ichthyofauna of the lagoon complex.

Spatial and temporal patterns of fish assemblages in Lake Nokoué and the Porto-Novo Lagoon (Benin, West Africa)

Laboratory of Hydrobiology and Aquaculture, Faculty of Agricultural Sciences

Laboratory of Animal Pathology, Microbiology and Immunology

Fisheries & Aquatic Life

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fish distribution in the lagoon complex of Lake Nokoué and Porto-Novo Lagoon is poorly documented. The lagoon complex is of particular interest because of its...

Spatial and temporal patterns of fish assemblages in Lake Nokoué and the Porto-Novo Lagoon (Benin, West Africa)

Online veröffentlicht: 07. Juni 2024

Seitenbereich: 9 - 25

Eingereicht: 21. März 2024

DOI: https://doi.org/10.2478/aopf-2024-0002

Schlüsselwörter
artificial neural networks, coastal river, fish communities, indicator value method, Ouémé River

© 2024 Lederoun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Spatial and temporal patterns of fish assemblages in Lake Nokoué and the Porto-Novo Lagoon (Benin, West Africa)

Online veröffentlicht: 07. Juni 2024

Seitenbereich: 9 - 25

Eingereicht: 21. März 2024

DOI: https://doi.org/10.2478/aopf-2024-0002

Schlüsselwörterartificial neural networks, coastal river, fish communities, indicator value method, Ouémé River

© 2024 Lederoun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
artificial neural networks, coastal river, fish communities, indicator value method, Ouémé River